Iris dataset with ggplot

This script explores the iris dataset using ggplot().

## add row number as a new column in iris for the hover text in ggplotly()
iris_plot <- ggplot(data = iris %>% rownames_to_column(),
                    aes(x = Sepal.Width, y = Sepal.Length)) +
  # regression line using lm, will be split by the facetting variable
  geom_smooth(method = "lm", colour = "black") +
  # add colors to the points
  # add the text aesthetic for ggplotly hover information: `text` is not usually an argument for geom_point(), that's why you get a warning. If you want to plot points with text, use geom_text() or geom_label()
  geom_point(aes(colour = Petal.Width >= 2, text = paste("row number: ", rowname)), 
             # transparency of points
             alpha = 0.5) + 
  xlab("Sepal width (cm)") +
  # specify the axis title either with xlab() and ylab() or 
  # with scale_x_... and scale_y_.. along with other settings like axis ticks
  scale_y_continuous(name = "Sepal length (cm)", 
                     # setting the upper limit to NA will use the data's own upper limit
                     limits = c(0, NA), 
                     # breaks specify the axis tick marks
                     breaks = seq(from = 0, to = 10, by = 1)) +
  # for facetting with one variable, use facet_wrap()
  # for two, use facet_grid()
  facet_wrap(~ Species) +
  # customize the colors for the geom_point() layer
  # there are many options, such as using color brewer
  scale_color_brewer(name = "Petal width >= 2cm", type = "qual", palette = "Set1") +
  # another option is to manually specify them for full control
  # for this you need to specify the color per category manually using a named character vector
  # e.g. cases that are TRUE will be dark red, and FALSE will be orange
  # to see this, uncomment the next line and comment out the scale_color_brewer() layer
  # scale_color_manual(name = "Petal width >= 2cm", values = c("TRUE" = "darkred", "FALSE" = "orange")) +
  # there are many plotting themes to choose from the ggthemes package
  theme_minimal() +
  # move the legend to the bottom
  theme(legend.position = "bottom") 

## Warning: Ignoring unknown aesthetics: text

# for interactive plots, there's a ggplotly() wrapper for ggplots
# ggplotly() overrides some of the ggplot() settings, like the legend position
# see here: https://github.com/ropensci/plotly/issues/1049
# to customize the legend for ggplotly(), use its layout() function...
ggplotly(iris_plot) %>% layout(legend = list(orientation = "h", x = -0.1, y =-0.2))